# New Workflow

## Overview

The first versions of TensorRT-LLM were developed with a very aggressive timeline. For those versions emphasis was not put
on defining a unified workflow. Now that TensorRT-LLM has reached some level of feature richness, the development team has
decided to put more efforts into unifying the APIs and workflow of TensorRT-LLM. This document summarises the new workflow
adopted by TensorRT-LLM at its core.

There are 3 steps in the new workflow:

1. Convert weights from different source frameworks into TensorRT-LLM checkpoint
2. Build the TensorRT-LLM checkpoint into TensorRT engine(s) with a unified build command
3. Load the engine(s) to TensorRT-LLM model runner and make evaluation with different evaluation tasks

```
NeMo -------------
                  |
HuggingFace ------
                  |   convert                             build                    load
AMMO -------------  ----------> TensorRT-LLM Checkpoint --------> TensorRT Engine ------> TensorRT-LLM ModelRunner
                  |
JAX --------------
                  |
DeepSpeed --------
```

## Prepare the TensorRT-LLM Checkpoint

TensorRT-LLM aims at supporting different of sources:

1. Trained models from NeMo, DeepSpeed, JAX
2. Quantized models from AMMO
3. Popular models from HuggingFace

TensorRT-LLM defines its own checkpoint format. A checkpoint directory includes:

1. One config json file, which contains several model hyper-parameters
2. One or several rank weights files, each file contains a dictionary of tensors (weights).
The different files will be loaded by different ranks in a multi-GPU (multi-process) scenario

### Config

| Field                                  | Type       | Default Value       |
| :------------------------------------- | :--------- | :------------------ |
| architecture                           | string     | mandatory           |
| dtype                                  | string     | mandatory           |
| logits_dtype                           | string     | 'float32'           |
| vocab_size                             | int        | mandatory           |
| max_position_embeddings                | int        | null                |
| hidden_size                            | int        | mandatory           |
| num_hidden_layers                      | int        | mandatory           |
| num_attention_heads                    | int        | mandatory           |
| num_key_value_heads                    | int        | num_attention_heads |
| hidden_act                             | string     | mandatory           |
| intermediate_size                      | int        | null                |
| norm_epsilon                           | float      | 1e-5                |
| position_embedding_type                | string     | 'learned_absolute'  |
| use_prompt_tuning                      | bool       | false               |
| mapping.world_size                     | int        | 1                   |
| mapping.tp_size                        | int        | 1                   |
| mapping.pp_size                        | int        | 1                   |
| quantization.quant_aglo                | str        | null                |
| quantization.kv_cache_quant_aglo       | str        | null                |
| quantization.group_size                | int        | 64                  |
| quantization.has_zero_point            | bool       | False               |
| quantization.pre_quant_scale           | bool       | False               |
| quantization.exclude_modules           | list       | null                |

`mapping.world_size` means `mapping` is a dictionary containing the `world_size` sub field.

```json
{
    "architecture": "OPTForCausalLM",
    "mapping": {
        "world_size": 1
    }
}
```

Supported quantization algorithm list:

- W8A16
- W4A16
- W4A16_AWQ
- W4A8_AWQ
- W4A16_GPTQ
- FP8
- W8A8_SQ_PER_CHANNEL

Supported KV cache quantization algorithm list:

- FP8
- INT8

The config field is extensible, a model could add its own specific config fields.
For example, OPT model has a `do_layer_norm_before` field.

Here is the model specific config list:

| Field                                  | Type       | Default Value       |
| :------------------------------------- | :--------- | :------------------ |
| OPT                                    |            |                     |
| do_layer_norm_before                   | bool       | False               |
|                                        |            |                     |
| Falcon                                 |            |                     |
| bias                                   | bool       | True                |
| new_decoder_architecture               | bool       | False               |
| parallel_attention                     | bool       | False               |

### Rank Weights

Like PyTorch, the tensor(weight) name is a string containing hierarchical information,
which is uniquely mapped to a certain parameter of a TensorRT-LLM model.

For example, each transformer layer of the OPT model contains an `Attention` layer, an `MLP` layer and two `LayerNorm` layers.

#### Attention Weights

The `Attention` layer contains two `Linear` layers, qkv and dense; each `Linear` layer contains one weight and one bias.
So, there are four tensors (weights) in total, whose names are:

- "transformer.layers.0.attention.qkv.weight"
- "transformer.layers.0.attention.qkv.bias"
- "transformer.layers.0.attention.dense.weight"
- "transformer.layers.0.attention.dense.bias"

where `transformer.layers.0.attention` is the prefix name, indicating that the weights/biases are in the attention module of the 0-th transformer layer.

#### MLP Weights

The `MLP` layer also contains two `Linear` layers, fc and proj; each `Linear` layer contains one weight and one bias.
So, there are four tensors (weights) in total, whose names are:

- "transformer.layers.0.mlp.fc.weight"
- "transformer.layers.0.mlp.fc.bias"
- "transformer.layers.0.mlp.proj.weight"
- "transformer.layers.0.mlp.proj.bias"

where `transformer.layers.0.mlp` is the prefix name, indicating that the weights/biases are in the mlp module of the 0-th transformer layer.

#### LayerNorm Weights

Each of the two `LayerNorm` layers, namely input_layernorm and post_layernorm, contains one weight and one bias.
So, there are four tensors (weights) in total, whose names are:

- "transformer.layers.0.input_layernorm.weight"
- "transformer.layers.0.input_layernorm.bias"
- "transformer.layers.0.post_layernorm.weight"
- "transformer.layers.0.post_layernorm.bias"

where `transformer.layers.0.input_layernorm` and `transformer.layers.0.post_layernorm` are prefix names for the two layernorm modules.

#### KV Cache Quantization Scaling Factors

Note that if we quantize the model, there will be different tensors (depending on the quantization method applied).
For example, if we quantize the KV cache, the `Attention` layer will have this extra scaling factor:

- "transformer.layers.0.attention.kv_cache_scaling_factor"

#### FP8 Quantization Scaling Factors

For example, here is the FP8 scaling factors of attention.qkv linear layer:

- "transformer.layers.0.attention.qkv.activation_scaling_factor"
- "transformer.layers.0.attention.qkv.weights_scaling_factor"

#### AWQ Quantization Scaling Factors

For example, here is the AWQ scaling factors of mlp.fc linear layer:

- "transformer.layers.0.mlp.fc.weights_scaling_factor"
- "transformer.layers.0.mlp.fc.prequant_scaling_factor"

**Note**: The linear weights in TensorRT-LLM checkpoint always follows (out_feature, in_feature) shape,
whereas some quantized linear in TensorRT-LLM implemented by plugin may use (in_feature, out_fature) shape.
`trtllm-build` command will add a transpose operation to post-process it.

### Example

Let's take OPT as an example, say we want to deploy the model with tensor parallelism 2:

```bash
cd examples/opt
python3 convert_checkpoint.py --model_dir ./opt-125m \
                --dtype float16 \
                --world_size 2 \
                --output_dir ./opt/125M/trt_ckpt/fp16/2-gpu/
```

Here is the checkpoint directory:

```
./opt/125M/trt_ckpt/fp16/1-gpu/
    config.json
    rank0.safetensors
    rank1.safetensors
```

Here is the `config.json`:

```json
{
    "architecture": "OPTForCausalLM",
    "dtype": "float16",
    "logits_dtype": "float32",
    "num_hidden_layers": 12,
    "num_attention_heads": 12,
    "hidden_size": 768,
    "vocab_size": 50272,
    "position_embedding_type": "learned_absolute",
    "max_position_embeddings": 2048,
    "hidden_act": "relu",
    "quantization": {
        "use_weight_only": false,
        "weight_only_precision": "int8"
    },
    "mapping": {
        "world_size": 2,
        "tp_size": 2
    },
    "use_parallel_embedding": false,
    "embedding_sharding_dim": 0,
    "share_embedding_table": false,
    "do_layer_norm_before": true,
    "use_prompt_tuning": false
}
```

## Build Checkpoint into TensorRT Engine

TensorRT-LLM provides a unified build command: `trtllm-build`. Before using it,
you may need to add it to the `PATH`

```bash
export PATH=/usr/local/bin:$PATH

trtllm-build --checkpoint_dir ./opt/125M/trt_ckpt/fp16/2-gpu/ \
                --gemm_plugin float16 \
                --max_batch_size 8 \
                --max_input_len 924 \
                --max_output_len 100 \
                --output_dir ./opt/125M/trt_engines/fp16/2-gpu/
```

## Make Evaluation

```bash
mpirun -n 2 --allow-run-as-root \
    python3 ../summarize.py --engine_dir ./opt/125M/trt_engines/fp16/2-gpu/ \
                        --batch_size 1 \
                        --test_trt_llm \
                        --hf_model_dir opt-125m \
                        --data_type fp16 \
                        --check_accuracy \
                        --tensorrt_llm_rouge1_threshold=14
```
